Cognitive Load Estimation with Behavioral Cues in Human-Machine Interaction
Abstract
Detecting human cognitive load is an increasingly important issue in the interaction between humans and machines, computers, and robots. In the past decade, several studies have sought to distinguish the cognitive load, or workload, state of humans based on multiple observations, such as behavioral, physiological, or multi-modal data. In the Human-Machine Interaction (HMI) cases, estimating human workload is essential because manipulators’ performance could be adversely affected when they have many tasks that may be demanding. If the workload level can be detected, it will be beneficial to reallocate tasks on manipulators to improve the productivity of HMI tasks. However, it is still on question marks what kinds of cues can be utilized to know the degree of workload. In this research, eye blinking and mouse tracking are chosen as behavioral cues, exploring the possibility of a non-intrusive and automated workload estimator. During tests, behavior cues are statistically analyzed to find the difference among levels, using a dataset focused on three levels of the dual n-back memory game. The statistically analyzed signal is trained in a deep neural network model to classify the workload level. In this study, eye blinking related data and mouse tracking data have been statistically analyzed. The one-way repeated measure analysis of variance test result showed eye blinking duration on the dual 1-back and 3-back are significantly different. The mouse tracking data could not pass the statistical test. A three-dimension convolutional deep neural network is used to train visual data of human behavior. Classifying the dual 1-back and 3-back data accuracy is 51% with 0.66 F1-score on 1-back and 0.14 on 3-back data. In conclusion, blinking and mouse tracking are unlikely helpful cues when estimating different levels of workload.
Degree
M.Sc.
Advisors
Min, Purdue University.
Subject Area
Artificial intelligence|Computer science|Neurosciences
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